Here we present a technique for automatic classification of seafloor data collected during the 2012 HABCAM-V4 cruises, a federally funded long term project part of the annual NOAA sea-scallop’s survey.

The analysis will be based on an unsupervised spatial clustering (K-means) of a combination of several predictors like morphological features (curvature, rugosity, fractal index, surface area) and backscatter intensity. The final results will be validated by analysing the identified classes with a randomly selected subset of underwater photographs for each class.

The seafloor classification map produced is then used as a preliminary "habitat classification" for further classification. It can be reused to define selection criteria for the underwater images used by automatic classifier or by manual image annotator tools.

Results from this project will also help to define new survey track-lines prior to and during HABCAM surveys.